I'm not sure I completely follow your point, but I sort of get it. I tend to think of symbols as one type of the "AI stuff" a computer uses to think with -- the other main type of "AI stuff" being neural networks. These have analogies to the "mind stuff" we use to think with.
On their own, symbols don't mean anything, of course, and inherently don't contain "understanding" in any definition of understanding. Is there a broad theory of symbols? We kind of proceed with loose definitions. I remember reading the Newell and Simon works, and they say AI strictly in terms of symbols and LISP (as I recall anyway). On 2/18/19, Jim Bromer <[email protected]> wrote: > Since I realized that the discrete vs weighted arguments are passe I > decided that thinking about symbol nets might be a better direction for me, > > 1. A symbol may be an abstracted 'image' of a (relatively) lower level > object or system. > An image may consist of a feature of the referent, it may be an icon of > the referent or it may be a compressed form of the referent. > 2. A symbol may be more like a 'label' for some object or system. > 3. A generalization may be represented as an image of what is being > generalized but it also may be more of a label. > 4. An 'image', as I am using the term, may be derived from a part or > feature of an object or from a part of a system but it may be used to refer > to the object or system. > 5. An image or label may be used to represent a greater system. A system > may take on different appearances from different vantage points, and > analogously, some features of interest may be relevant in one context but > not from another context. A symbol may be correlated with some other > 'object' and may stand as a referent to it in some contexts. > > So, while some symbols may be applied to or projected onto a 'lower' corpus > of data, others would need to use an image to project onto the data field. > I use the term, 'lower' somewhat ambiguously, because I think it is useful > to symbolize a system of symbols so a 'higher' abstraction of a system > might also be used at the same level. And it seems that a label would have > to be associated with some images if it was to be projected against the > data. > > One other thing. This idea of projecting a symbol image onto some data, in > order to compare the image with some features of the data, seems like it > has fallen out of favor with the advancements of dlnns and other kinds of > neural nets. Projection seems like such a fundamental process that I cannot > see why it should be discarded just because it would be relatively slow > when used with symbol nets. And, there are exceptions, GPUs, for example, > love projecting one image onto another. > Jim Bromer ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Tcc0e554e7141c02f-Maa49d44013a0cd45ae209ec7 Delivery options: https://agi.topicbox.com/groups/agi/subscription
